# Importing the necessary libraries
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
# Assigning name to the dataset and calling the dataset
dataset1 = pd.read_csv("C:\\Users\\HP\\Downloads\\GL_files\\project_oct_new\\parkinsons.csv")
dataset1 # Printing the dataset
| name | MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | phon_R01_S01_1 | 119.992 | 157.302 | 74.997 | 0.00784 | 0.00007 | 0.00370 | 0.00554 | 0.01109 | 0.04374 | ... | 0.06545 | 0.02211 | 21.033 | 1 | 0.414783 | 0.815285 | -4.813031 | 0.266482 | 2.301442 | 0.284654 |
| 1 | phon_R01_S01_2 | 122.400 | 148.650 | 113.819 | 0.00968 | 0.00008 | 0.00465 | 0.00696 | 0.01394 | 0.06134 | ... | 0.09403 | 0.01929 | 19.085 | 1 | 0.458359 | 0.819521 | -4.075192 | 0.335590 | 2.486855 | 0.368674 |
| 2 | phon_R01_S01_3 | 116.682 | 131.111 | 111.555 | 0.01050 | 0.00009 | 0.00544 | 0.00781 | 0.01633 | 0.05233 | ... | 0.08270 | 0.01309 | 20.651 | 1 | 0.429895 | 0.825288 | -4.443179 | 0.311173 | 2.342259 | 0.332634 |
| 3 | phon_R01_S01_4 | 116.676 | 137.871 | 111.366 | 0.00997 | 0.00009 | 0.00502 | 0.00698 | 0.01505 | 0.05492 | ... | 0.08771 | 0.01353 | 20.644 | 1 | 0.434969 | 0.819235 | -4.117501 | 0.334147 | 2.405554 | 0.368975 |
| 4 | phon_R01_S01_5 | 116.014 | 141.781 | 110.655 | 0.01284 | 0.00011 | 0.00655 | 0.00908 | 0.01966 | 0.06425 | ... | 0.10470 | 0.01767 | 19.649 | 1 | 0.417356 | 0.823484 | -3.747787 | 0.234513 | 2.332180 | 0.410335 |
| 5 | phon_R01_S01_6 | 120.552 | 131.162 | 113.787 | 0.00968 | 0.00008 | 0.00463 | 0.00750 | 0.01388 | 0.04701 | ... | 0.06985 | 0.01222 | 21.378 | 1 | 0.415564 | 0.825069 | -4.242867 | 0.299111 | 2.187560 | 0.357775 |
| 6 | phon_R01_S02_1 | 120.267 | 137.244 | 114.820 | 0.00333 | 0.00003 | 0.00155 | 0.00202 | 0.00466 | 0.01608 | ... | 0.02337 | 0.00607 | 24.886 | 1 | 0.596040 | 0.764112 | -5.634322 | 0.257682 | 1.854785 | 0.211756 |
| 7 | phon_R01_S02_2 | 107.332 | 113.840 | 104.315 | 0.00290 | 0.00003 | 0.00144 | 0.00182 | 0.00431 | 0.01567 | ... | 0.02487 | 0.00344 | 26.892 | 1 | 0.637420 | 0.763262 | -6.167603 | 0.183721 | 2.064693 | 0.163755 |
| 8 | phon_R01_S02_3 | 95.730 | 132.068 | 91.754 | 0.00551 | 0.00006 | 0.00293 | 0.00332 | 0.00880 | 0.02093 | ... | 0.03218 | 0.01070 | 21.812 | 1 | 0.615551 | 0.773587 | -5.498678 | 0.327769 | 2.322511 | 0.231571 |
| 9 | phon_R01_S02_4 | 95.056 | 120.103 | 91.226 | 0.00532 | 0.00006 | 0.00268 | 0.00332 | 0.00803 | 0.02838 | ... | 0.04324 | 0.01022 | 21.862 | 1 | 0.547037 | 0.798463 | -5.011879 | 0.325996 | 2.432792 | 0.271362 |
| 10 | phon_R01_S02_5 | 88.333 | 112.240 | 84.072 | 0.00505 | 0.00006 | 0.00254 | 0.00330 | 0.00763 | 0.02143 | ... | 0.03237 | 0.01166 | 21.118 | 1 | 0.611137 | 0.776156 | -5.249770 | 0.391002 | 2.407313 | 0.249740 |
| 11 | phon_R01_S02_6 | 91.904 | 115.871 | 86.292 | 0.00540 | 0.00006 | 0.00281 | 0.00336 | 0.00844 | 0.02752 | ... | 0.04272 | 0.01141 | 21.414 | 1 | 0.583390 | 0.792520 | -4.960234 | 0.363566 | 2.642476 | 0.275931 |
| 12 | phon_R01_S04_1 | 136.926 | 159.866 | 131.276 | 0.00293 | 0.00002 | 0.00118 | 0.00153 | 0.00355 | 0.01259 | ... | 0.01968 | 0.00581 | 25.703 | 1 | 0.460600 | 0.646846 | -6.547148 | 0.152813 | 2.041277 | 0.138512 |
| 13 | phon_R01_S04_2 | 139.173 | 179.139 | 76.556 | 0.00390 | 0.00003 | 0.00165 | 0.00208 | 0.00496 | 0.01642 | ... | 0.02184 | 0.01041 | 24.889 | 1 | 0.430166 | 0.665833 | -5.660217 | 0.254989 | 2.519422 | 0.199889 |
| 14 | phon_R01_S04_3 | 152.845 | 163.305 | 75.836 | 0.00294 | 0.00002 | 0.00121 | 0.00149 | 0.00364 | 0.01828 | ... | 0.03191 | 0.00609 | 24.922 | 1 | 0.474791 | 0.654027 | -6.105098 | 0.203653 | 2.125618 | 0.170100 |
| 15 | phon_R01_S04_4 | 142.167 | 217.455 | 83.159 | 0.00369 | 0.00003 | 0.00157 | 0.00203 | 0.00471 | 0.01503 | ... | 0.02316 | 0.00839 | 25.175 | 1 | 0.565924 | 0.658245 | -5.340115 | 0.210185 | 2.205546 | 0.234589 |
| 16 | phon_R01_S04_5 | 144.188 | 349.259 | 82.764 | 0.00544 | 0.00004 | 0.00211 | 0.00292 | 0.00632 | 0.02047 | ... | 0.02908 | 0.01859 | 22.333 | 1 | 0.567380 | 0.644692 | -5.440040 | 0.239764 | 2.264501 | 0.218164 |
| 17 | phon_R01_S04_6 | 168.778 | 232.181 | 75.603 | 0.00718 | 0.00004 | 0.00284 | 0.00387 | 0.00853 | 0.03327 | ... | 0.04322 | 0.02919 | 20.376 | 1 | 0.631099 | 0.605417 | -2.931070 | 0.434326 | 3.007463 | 0.430788 |
| 18 | phon_R01_S05_1 | 153.046 | 175.829 | 68.623 | 0.00742 | 0.00005 | 0.00364 | 0.00432 | 0.01092 | 0.05517 | ... | 0.07413 | 0.03160 | 17.280 | 1 | 0.665318 | 0.719467 | -3.949079 | 0.357870 | 3.109010 | 0.377429 |
| 19 | phon_R01_S05_2 | 156.405 | 189.398 | 142.822 | 0.00768 | 0.00005 | 0.00372 | 0.00399 | 0.01116 | 0.03995 | ... | 0.05164 | 0.03365 | 17.153 | 1 | 0.649554 | 0.686080 | -4.554466 | 0.340176 | 2.856676 | 0.322111 |
| 20 | phon_R01_S05_3 | 153.848 | 165.738 | 65.782 | 0.00840 | 0.00005 | 0.00428 | 0.00450 | 0.01285 | 0.03810 | ... | 0.05000 | 0.03871 | 17.536 | 1 | 0.660125 | 0.704087 | -4.095442 | 0.262564 | 2.739710 | 0.365391 |
| 21 | phon_R01_S05_4 | 153.880 | 172.860 | 78.128 | 0.00480 | 0.00003 | 0.00232 | 0.00267 | 0.00696 | 0.04137 | ... | 0.06062 | 0.01849 | 19.493 | 1 | 0.629017 | 0.698951 | -5.186960 | 0.237622 | 2.557536 | 0.259765 |
| 22 | phon_R01_S05_5 | 167.930 | 193.221 | 79.068 | 0.00442 | 0.00003 | 0.00220 | 0.00247 | 0.00661 | 0.04351 | ... | 0.06685 | 0.01280 | 22.468 | 1 | 0.619060 | 0.679834 | -4.330956 | 0.262384 | 2.916777 | 0.285695 |
| 23 | phon_R01_S05_6 | 173.917 | 192.735 | 86.180 | 0.00476 | 0.00003 | 0.00221 | 0.00258 | 0.00663 | 0.04192 | ... | 0.06562 | 0.01840 | 20.422 | 1 | 0.537264 | 0.686894 | -5.248776 | 0.210279 | 2.547508 | 0.253556 |
| 24 | phon_R01_S06_1 | 163.656 | 200.841 | 76.779 | 0.00742 | 0.00005 | 0.00380 | 0.00390 | 0.01140 | 0.01659 | ... | 0.02214 | 0.01778 | 23.831 | 1 | 0.397937 | 0.732479 | -5.557447 | 0.220890 | 2.692176 | 0.215961 |
| 25 | phon_R01_S06_2 | 104.400 | 206.002 | 77.968 | 0.00633 | 0.00006 | 0.00316 | 0.00375 | 0.00948 | 0.03767 | ... | 0.05197 | 0.02887 | 22.066 | 1 | 0.522746 | 0.737948 | -5.571843 | 0.236853 | 2.846369 | 0.219514 |
| 26 | phon_R01_S06_3 | 171.041 | 208.313 | 75.501 | 0.00455 | 0.00003 | 0.00250 | 0.00234 | 0.00750 | 0.01966 | ... | 0.02666 | 0.01095 | 25.908 | 1 | 0.418622 | 0.720916 | -6.183590 | 0.226278 | 2.589702 | 0.147403 |
| 27 | phon_R01_S06_4 | 146.845 | 208.701 | 81.737 | 0.00496 | 0.00003 | 0.00250 | 0.00275 | 0.00749 | 0.01919 | ... | 0.02650 | 0.01328 | 25.119 | 1 | 0.358773 | 0.726652 | -6.271690 | 0.196102 | 2.314209 | 0.162999 |
| 28 | phon_R01_S06_5 | 155.358 | 227.383 | 80.055 | 0.00310 | 0.00002 | 0.00159 | 0.00176 | 0.00476 | 0.01718 | ... | 0.02307 | 0.00677 | 25.970 | 1 | 0.470478 | 0.676258 | -7.120925 | 0.279789 | 2.241742 | 0.108514 |
| 29 | phon_R01_S06_6 | 162.568 | 198.346 | 77.630 | 0.00502 | 0.00003 | 0.00280 | 0.00253 | 0.00841 | 0.01791 | ... | 0.02380 | 0.01170 | 25.678 | 1 | 0.427785 | 0.723797 | -6.635729 | 0.209866 | 1.957961 | 0.135242 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 165 | phon_R01_S42_1 | 236.200 | 244.663 | 102.137 | 0.00277 | 0.00001 | 0.00154 | 0.00153 | 0.00462 | 0.02448 | ... | 0.04231 | 0.00620 | 24.078 | 0 | 0.469928 | 0.628232 | -6.816086 | 0.172270 | 2.235197 | 0.119652 |
| 166 | phon_R01_S42_2 | 237.323 | 243.709 | 229.256 | 0.00303 | 0.00001 | 0.00173 | 0.00159 | 0.00519 | 0.01242 | ... | 0.02089 | 0.00533 | 24.679 | 0 | 0.384868 | 0.626710 | -7.018057 | 0.176316 | 1.852402 | 0.091604 |
| 167 | phon_R01_S42_3 | 260.105 | 264.919 | 237.303 | 0.00339 | 0.00001 | 0.00205 | 0.00186 | 0.00616 | 0.02030 | ... | 0.03557 | 0.00910 | 21.083 | 0 | 0.440988 | 0.628058 | -7.517934 | 0.160414 | 1.881767 | 0.075587 |
| 168 | phon_R01_S42_4 | 197.569 | 217.627 | 90.794 | 0.00803 | 0.00004 | 0.00490 | 0.00448 | 0.01470 | 0.02177 | ... | 0.03836 | 0.01337 | 19.269 | 0 | 0.372222 | 0.725216 | -5.736781 | 0.164529 | 2.882450 | 0.202879 |
| 169 | phon_R01_S42_5 | 240.301 | 245.135 | 219.783 | 0.00517 | 0.00002 | 0.00316 | 0.00283 | 0.00949 | 0.02018 | ... | 0.03529 | 0.00965 | 21.020 | 0 | 0.371837 | 0.646167 | -7.169701 | 0.073298 | 2.266432 | 0.100881 |
| 170 | phon_R01_S42_6 | 244.990 | 272.210 | 239.170 | 0.00451 | 0.00002 | 0.00279 | 0.00237 | 0.00837 | 0.01897 | ... | 0.03253 | 0.01049 | 21.528 | 0 | 0.522812 | 0.646818 | -7.304500 | 0.171088 | 2.095237 | 0.096220 |
| 171 | phon_R01_S43_1 | 112.547 | 133.374 | 105.715 | 0.00355 | 0.00003 | 0.00166 | 0.00190 | 0.00499 | 0.01358 | ... | 0.01992 | 0.00435 | 26.436 | 0 | 0.413295 | 0.756700 | -6.323531 | 0.218885 | 2.193412 | 0.160376 |
| 172 | phon_R01_S43_2 | 110.739 | 113.597 | 100.139 | 0.00356 | 0.00003 | 0.00170 | 0.00200 | 0.00510 | 0.01484 | ... | 0.02261 | 0.00430 | 26.550 | 0 | 0.369090 | 0.776158 | -6.085567 | 0.192375 | 1.889002 | 0.174152 |
| 173 | phon_R01_S43_3 | 113.715 | 116.443 | 96.913 | 0.00349 | 0.00003 | 0.00171 | 0.00203 | 0.00514 | 0.01472 | ... | 0.02245 | 0.00478 | 26.547 | 0 | 0.380253 | 0.766700 | -5.943501 | 0.192150 | 1.852542 | 0.179677 |
| 174 | phon_R01_S43_4 | 117.004 | 144.466 | 99.923 | 0.00353 | 0.00003 | 0.00176 | 0.00218 | 0.00528 | 0.01657 | ... | 0.02643 | 0.00590 | 25.445 | 0 | 0.387482 | 0.756482 | -6.012559 | 0.229298 | 1.872946 | 0.163118 |
| 175 | phon_R01_S43_5 | 115.380 | 123.109 | 108.634 | 0.00332 | 0.00003 | 0.00160 | 0.00199 | 0.00480 | 0.01503 | ... | 0.02436 | 0.00401 | 26.005 | 0 | 0.405991 | 0.761255 | -5.966779 | 0.197938 | 1.974857 | 0.184067 |
| 176 | phon_R01_S43_6 | 116.388 | 129.038 | 108.970 | 0.00346 | 0.00003 | 0.00169 | 0.00213 | 0.00507 | 0.01725 | ... | 0.02623 | 0.00415 | 26.143 | 0 | 0.361232 | 0.763242 | -6.016891 | 0.109256 | 2.004719 | 0.174429 |
| 177 | phon_R01_S44_1 | 151.737 | 190.204 | 129.859 | 0.00314 | 0.00002 | 0.00135 | 0.00162 | 0.00406 | 0.01469 | ... | 0.02184 | 0.00570 | 24.151 | 1 | 0.396610 | 0.745957 | -6.486822 | 0.197919 | 2.449763 | 0.132703 |
| 178 | phon_R01_S44_2 | 148.790 | 158.359 | 138.990 | 0.00309 | 0.00002 | 0.00152 | 0.00186 | 0.00456 | 0.01574 | ... | 0.02518 | 0.00488 | 24.412 | 1 | 0.402591 | 0.762508 | -6.311987 | 0.182459 | 2.251553 | 0.160306 |
| 179 | phon_R01_S44_3 | 148.143 | 155.982 | 135.041 | 0.00392 | 0.00003 | 0.00204 | 0.00231 | 0.00612 | 0.01450 | ... | 0.02175 | 0.00540 | 23.683 | 1 | 0.398499 | 0.778349 | -5.711205 | 0.240875 | 2.845109 | 0.192730 |
| 180 | phon_R01_S44_4 | 150.440 | 163.441 | 144.736 | 0.00396 | 0.00003 | 0.00206 | 0.00233 | 0.00619 | 0.02551 | ... | 0.03964 | 0.00611 | 23.133 | 1 | 0.352396 | 0.759320 | -6.261446 | 0.183218 | 2.264226 | 0.144105 |
| 181 | phon_R01_S44_5 | 148.462 | 161.078 | 141.998 | 0.00397 | 0.00003 | 0.00202 | 0.00235 | 0.00605 | 0.01831 | ... | 0.02849 | 0.00639 | 22.866 | 1 | 0.408598 | 0.768845 | -5.704053 | 0.216204 | 2.679185 | 0.197710 |
| 182 | phon_R01_S44_6 | 149.818 | 163.417 | 144.786 | 0.00336 | 0.00002 | 0.00174 | 0.00198 | 0.00521 | 0.02145 | ... | 0.03464 | 0.00595 | 23.008 | 1 | 0.329577 | 0.757180 | -6.277170 | 0.109397 | 2.209021 | 0.156368 |
| 183 | phon_R01_S49_1 | 117.226 | 123.925 | 106.656 | 0.00417 | 0.00004 | 0.00186 | 0.00270 | 0.00558 | 0.01909 | ... | 0.02592 | 0.00955 | 23.079 | 0 | 0.603515 | 0.669565 | -5.619070 | 0.191576 | 2.027228 | 0.215724 |
| 184 | phon_R01_S49_2 | 116.848 | 217.552 | 99.503 | 0.00531 | 0.00005 | 0.00260 | 0.00346 | 0.00780 | 0.01795 | ... | 0.02429 | 0.01179 | 22.085 | 0 | 0.663842 | 0.656516 | -5.198864 | 0.206768 | 2.120412 | 0.252404 |
| 185 | phon_R01_S49_3 | 116.286 | 177.291 | 96.983 | 0.00314 | 0.00003 | 0.00134 | 0.00192 | 0.00403 | 0.01564 | ... | 0.02001 | 0.00737 | 24.199 | 0 | 0.598515 | 0.654331 | -5.592584 | 0.133917 | 2.058658 | 0.214346 |
| 186 | phon_R01_S49_4 | 116.556 | 592.030 | 86.228 | 0.00496 | 0.00004 | 0.00254 | 0.00263 | 0.00762 | 0.01660 | ... | 0.02460 | 0.01397 | 23.958 | 0 | 0.566424 | 0.667654 | -6.431119 | 0.153310 | 2.161936 | 0.120605 |
| 187 | phon_R01_S49_5 | 116.342 | 581.289 | 94.246 | 0.00267 | 0.00002 | 0.00115 | 0.00148 | 0.00345 | 0.01300 | ... | 0.01892 | 0.00680 | 25.023 | 0 | 0.528485 | 0.663884 | -6.359018 | 0.116636 | 2.152083 | 0.138868 |
| 188 | phon_R01_S49_6 | 114.563 | 119.167 | 86.647 | 0.00327 | 0.00003 | 0.00146 | 0.00184 | 0.00439 | 0.01185 | ... | 0.01672 | 0.00703 | 24.775 | 0 | 0.555303 | 0.659132 | -6.710219 | 0.149694 | 1.913990 | 0.121777 |
| 189 | phon_R01_S50_1 | 201.774 | 262.707 | 78.228 | 0.00694 | 0.00003 | 0.00412 | 0.00396 | 0.01235 | 0.02574 | ... | 0.04363 | 0.04441 | 19.368 | 0 | 0.508479 | 0.683761 | -6.934474 | 0.159890 | 2.316346 | 0.112838 |
| 190 | phon_R01_S50_2 | 174.188 | 230.978 | 94.261 | 0.00459 | 0.00003 | 0.00263 | 0.00259 | 0.00790 | 0.04087 | ... | 0.07008 | 0.02764 | 19.517 | 0 | 0.448439 | 0.657899 | -6.538586 | 0.121952 | 2.657476 | 0.133050 |
| 191 | phon_R01_S50_3 | 209.516 | 253.017 | 89.488 | 0.00564 | 0.00003 | 0.00331 | 0.00292 | 0.00994 | 0.02751 | ... | 0.04812 | 0.01810 | 19.147 | 0 | 0.431674 | 0.683244 | -6.195325 | 0.129303 | 2.784312 | 0.168895 |
| 192 | phon_R01_S50_4 | 174.688 | 240.005 | 74.287 | 0.01360 | 0.00008 | 0.00624 | 0.00564 | 0.01873 | 0.02308 | ... | 0.03804 | 0.10715 | 17.883 | 0 | 0.407567 | 0.655683 | -6.787197 | 0.158453 | 2.679772 | 0.131728 |
| 193 | phon_R01_S50_5 | 198.764 | 396.961 | 74.904 | 0.00740 | 0.00004 | 0.00370 | 0.00390 | 0.01109 | 0.02296 | ... | 0.03794 | 0.07223 | 19.020 | 0 | 0.451221 | 0.643956 | -6.744577 | 0.207454 | 2.138608 | 0.123306 |
| 194 | phon_R01_S50_6 | 214.289 | 260.277 | 77.973 | 0.00567 | 0.00003 | 0.00295 | 0.00317 | 0.00885 | 0.01884 | ... | 0.03078 | 0.04398 | 21.209 | 0 | 0.462803 | 0.664357 | -5.724056 | 0.190667 | 2.555477 | 0.148569 |
195 rows × 24 columns
# for features in dataset1.columns:
# if dataset1[features].dtype == 'object':
# dataset1[features] = pd.Categorical(dataset1[features]).codes
dataset2= dataset1.drop(['name'],axis =1)
dataset2.shape # Analysis on the dimension of the dataset
(195, 23)
dataset2.dtypes # Analysing the dataset datatypes
# There are int and float datatypes
# No object datatypes are being present
MDVP:Fo(Hz) float64 MDVP:Fhi(Hz) float64 MDVP:Flo(Hz) float64 MDVP:Jitter(%) float64 MDVP:Jitter(Abs) float64 MDVP:RAP float64 MDVP:PPQ float64 Jitter:DDP float64 MDVP:Shimmer float64 MDVP:Shimmer(dB) float64 Shimmer:APQ3 float64 Shimmer:APQ5 float64 MDVP:APQ float64 Shimmer:DDA float64 NHR float64 HNR float64 status int64 RPDE float64 DFA float64 spread1 float64 spread2 float64 D2 float64 PPE float64 dtype: object
dataset2.isnull().sum() #Code to check whether any missing values are present on the dataset
# and to get it treated
MDVP:Fo(Hz) 0 MDVP:Fhi(Hz) 0 MDVP:Flo(Hz) 0 MDVP:Jitter(%) 0 MDVP:Jitter(Abs) 0 MDVP:RAP 0 MDVP:PPQ 0 Jitter:DDP 0 MDVP:Shimmer 0 MDVP:Shimmer(dB) 0 Shimmer:APQ3 0 Shimmer:APQ5 0 MDVP:APQ 0 Shimmer:DDA 0 NHR 0 HNR 0 status 0 RPDE 0 DFA 0 spread1 0 spread2 0 D2 0 PPE 0 dtype: int64
dataset2.head(10) # inital 10 head datas of the Parkinsons dataset to understand the value distribution
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 119.992 | 157.302 | 74.997 | 0.00784 | 0.00007 | 0.00370 | 0.00554 | 0.01109 | 0.04374 | 0.426 | ... | 0.06545 | 0.02211 | 21.033 | 1 | 0.414783 | 0.815285 | -4.813031 | 0.266482 | 2.301442 | 0.284654 |
| 1 | 122.400 | 148.650 | 113.819 | 0.00968 | 0.00008 | 0.00465 | 0.00696 | 0.01394 | 0.06134 | 0.626 | ... | 0.09403 | 0.01929 | 19.085 | 1 | 0.458359 | 0.819521 | -4.075192 | 0.335590 | 2.486855 | 0.368674 |
| 2 | 116.682 | 131.111 | 111.555 | 0.01050 | 0.00009 | 0.00544 | 0.00781 | 0.01633 | 0.05233 | 0.482 | ... | 0.08270 | 0.01309 | 20.651 | 1 | 0.429895 | 0.825288 | -4.443179 | 0.311173 | 2.342259 | 0.332634 |
| 3 | 116.676 | 137.871 | 111.366 | 0.00997 | 0.00009 | 0.00502 | 0.00698 | 0.01505 | 0.05492 | 0.517 | ... | 0.08771 | 0.01353 | 20.644 | 1 | 0.434969 | 0.819235 | -4.117501 | 0.334147 | 2.405554 | 0.368975 |
| 4 | 116.014 | 141.781 | 110.655 | 0.01284 | 0.00011 | 0.00655 | 0.00908 | 0.01966 | 0.06425 | 0.584 | ... | 0.10470 | 0.01767 | 19.649 | 1 | 0.417356 | 0.823484 | -3.747787 | 0.234513 | 2.332180 | 0.410335 |
| 5 | 120.552 | 131.162 | 113.787 | 0.00968 | 0.00008 | 0.00463 | 0.00750 | 0.01388 | 0.04701 | 0.456 | ... | 0.06985 | 0.01222 | 21.378 | 1 | 0.415564 | 0.825069 | -4.242867 | 0.299111 | 2.187560 | 0.357775 |
| 6 | 120.267 | 137.244 | 114.820 | 0.00333 | 0.00003 | 0.00155 | 0.00202 | 0.00466 | 0.01608 | 0.140 | ... | 0.02337 | 0.00607 | 24.886 | 1 | 0.596040 | 0.764112 | -5.634322 | 0.257682 | 1.854785 | 0.211756 |
| 7 | 107.332 | 113.840 | 104.315 | 0.00290 | 0.00003 | 0.00144 | 0.00182 | 0.00431 | 0.01567 | 0.134 | ... | 0.02487 | 0.00344 | 26.892 | 1 | 0.637420 | 0.763262 | -6.167603 | 0.183721 | 2.064693 | 0.163755 |
| 8 | 95.730 | 132.068 | 91.754 | 0.00551 | 0.00006 | 0.00293 | 0.00332 | 0.00880 | 0.02093 | 0.191 | ... | 0.03218 | 0.01070 | 21.812 | 1 | 0.615551 | 0.773587 | -5.498678 | 0.327769 | 2.322511 | 0.231571 |
| 9 | 95.056 | 120.103 | 91.226 | 0.00532 | 0.00006 | 0.00268 | 0.00332 | 0.00803 | 0.02838 | 0.255 | ... | 0.04324 | 0.01022 | 21.862 | 1 | 0.547037 | 0.798463 | -5.011879 | 0.325996 | 2.432792 | 0.271362 |
10 rows × 23 columns
dataset2.describe() # describe function could provide the distribution of the values of the dataset to study the mean, median and outliers presented on the dataset
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | ... | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 | 195.000000 |
| mean | 154.228641 | 197.104918 | 116.324631 | 0.006220 | 0.000044 | 0.003306 | 0.003446 | 0.009920 | 0.029709 | 0.282251 | ... | 0.046993 | 0.024847 | 21.885974 | 0.753846 | 0.498536 | 0.718099 | -5.684397 | 0.226510 | 2.381826 | 0.206552 |
| std | 41.390065 | 91.491548 | 43.521413 | 0.004848 | 0.000035 | 0.002968 | 0.002759 | 0.008903 | 0.018857 | 0.194877 | ... | 0.030459 | 0.040418 | 4.425764 | 0.431878 | 0.103942 | 0.055336 | 1.090208 | 0.083406 | 0.382799 | 0.090119 |
| min | 88.333000 | 102.145000 | 65.476000 | 0.001680 | 0.000007 | 0.000680 | 0.000920 | 0.002040 | 0.009540 | 0.085000 | ... | 0.013640 | 0.000650 | 8.441000 | 0.000000 | 0.256570 | 0.574282 | -7.964984 | 0.006274 | 1.423287 | 0.044539 |
| 25% | 117.572000 | 134.862500 | 84.291000 | 0.003460 | 0.000020 | 0.001660 | 0.001860 | 0.004985 | 0.016505 | 0.148500 | ... | 0.024735 | 0.005925 | 19.198000 | 1.000000 | 0.421306 | 0.674758 | -6.450096 | 0.174351 | 2.099125 | 0.137451 |
| 50% | 148.790000 | 175.829000 | 104.315000 | 0.004940 | 0.000030 | 0.002500 | 0.002690 | 0.007490 | 0.022970 | 0.221000 | ... | 0.038360 | 0.011660 | 22.085000 | 1.000000 | 0.495954 | 0.722254 | -5.720868 | 0.218885 | 2.361532 | 0.194052 |
| 75% | 182.769000 | 224.205500 | 140.018500 | 0.007365 | 0.000060 | 0.003835 | 0.003955 | 0.011505 | 0.037885 | 0.350000 | ... | 0.060795 | 0.025640 | 25.075500 | 1.000000 | 0.587562 | 0.761881 | -5.046192 | 0.279234 | 2.636456 | 0.252980 |
| max | 260.105000 | 592.030000 | 239.170000 | 0.033160 | 0.000260 | 0.021440 | 0.019580 | 0.064330 | 0.119080 | 1.302000 | ... | 0.169420 | 0.314820 | 33.047000 | 1.000000 | 0.685151 | 0.825288 | -2.434031 | 0.450493 | 3.671155 | 0.527367 |
8 rows × 23 columns
figure1=plt.figure(figsize = [20,20]) # plt.figure for the figure creation
ax= figure1.gca() # This code will get axes instance on the current figure matching the keyword argws
dataset2.hist(ax=ax) # The figure procedures are passed as the arguments to the histogram plot
# The histogram plot is being applied to the dataset1
plt.show() # For showing all the plots, this function being called
C:\Users\HP\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3296: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared exec(code_obj, self.user_global_ns, self.user_ns)
dataset2.isnull().any() # any() will return the boolean values of the columns on the dataset1
MDVP:Fo(Hz) False MDVP:Fhi(Hz) False MDVP:Flo(Hz) False MDVP:Jitter(%) False MDVP:Jitter(Abs) False MDVP:RAP False MDVP:PPQ False Jitter:DDP False MDVP:Shimmer False MDVP:Shimmer(dB) False Shimmer:APQ3 False Shimmer:APQ5 False MDVP:APQ False Shimmer:DDA False NHR False HNR False status False RPDE False DFA False spread1 False spread2 False D2 False PPE False dtype: bool
dataset2.columns
Index(['MDVP:Fo(Hz)', 'MDVP:Fhi(Hz)', 'MDVP:Flo(Hz)', 'MDVP:Jitter(%)',
'MDVP:Jitter(Abs)', 'MDVP:RAP', 'MDVP:PPQ', 'Jitter:DDP',
'MDVP:Shimmer', 'MDVP:Shimmer(dB)', 'Shimmer:APQ3', 'Shimmer:APQ5',
'MDVP:APQ', 'Shimmer:DDA', 'NHR', 'HNR', 'status', 'RPDE', 'DFA',
'spread1', 'spread2', 'D2', 'PPE'],
dtype='object')
dataset2.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 195 entries, 0 to 194 Data columns (total 23 columns): MDVP:Fo(Hz) 195 non-null float64 MDVP:Fhi(Hz) 195 non-null float64 MDVP:Flo(Hz) 195 non-null float64 MDVP:Jitter(%) 195 non-null float64 MDVP:Jitter(Abs) 195 non-null float64 MDVP:RAP 195 non-null float64 MDVP:PPQ 195 non-null float64 Jitter:DDP 195 non-null float64 MDVP:Shimmer 195 non-null float64 MDVP:Shimmer(dB) 195 non-null float64 Shimmer:APQ3 195 non-null float64 Shimmer:APQ5 195 non-null float64 MDVP:APQ 195 non-null float64 Shimmer:DDA 195 non-null float64 NHR 195 non-null float64 HNR 195 non-null float64 status 195 non-null int64 RPDE 195 non-null float64 DFA 195 non-null float64 spread1 195 non-null float64 spread2 195 non-null float64 D2 195 non-null float64 PPE 195 non-null float64 dtypes: float64(22), int64(1) memory usage: 35.1 KB
dataset2["status"].unique()
array([1, 0], dtype=int64)
dataset2.status.value_counts()
# dataset1["status"].value_counts() --> this is also prooduce the above results
1 147 0 48 Name: status, dtype: int64
dataset2[dataset2['status']==1].describe() #Study for the people with chance of Parkinsons disease
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | ... | 147.000000 | 147.000000 | 147.000000 | 147.0 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 | 147.000000 |
| mean | 145.180762 | 188.441463 | 106.893558 | 0.006989 | 0.000051 | 0.003757 | 0.003900 | 0.011273 | 0.033658 | 0.321204 | ... | 0.053027 | 0.029211 | 20.974048 | 1.0 | 0.516816 | 0.725408 | -5.333420 | 0.248133 | 2.456058 | 0.233828 |
| std | 32.348050 | 88.339180 | 32.274358 | 0.005240 | 0.000037 | 0.003241 | 0.002998 | 0.009724 | 0.019970 | 0.207798 | ... | 0.032391 | 0.044447 | 4.339143 | 0.0 | 0.101254 | 0.054786 | 0.970792 | 0.077809 | 0.375742 | 0.084271 |
| min | 88.333000 | 102.145000 | 65.476000 | 0.001680 | 0.000010 | 0.000680 | 0.000920 | 0.002040 | 0.010220 | 0.090000 | ... | 0.013640 | 0.002310 | 8.441000 | 1.0 | 0.263654 | 0.574282 | -7.120925 | 0.063412 | 1.765957 | 0.093193 |
| 25% | 117.572000 | 133.776500 | 80.875500 | 0.004005 | 0.000030 | 0.002030 | 0.002190 | 0.006085 | 0.018295 | 0.168000 | ... | 0.027400 | 0.008445 | 18.782000 | 1.0 | 0.439064 | 0.685569 | -6.038300 | 0.199507 | 2.180933 | 0.170103 |
| 50% | 145.174000 | 163.335000 | 99.770000 | 0.005440 | 0.000040 | 0.002840 | 0.003140 | 0.008530 | 0.028380 | 0.263000 | ... | 0.044510 | 0.016580 | 21.414000 | 1.0 | 0.530529 | 0.726652 | -5.440040 | 0.240875 | 2.439597 | 0.222716 |
| 75% | 170.071000 | 207.160500 | 129.240000 | 0.007670 | 0.000060 | 0.004100 | 0.004360 | 0.012300 | 0.042525 | 0.394500 | ... | 0.068455 | 0.027960 | 24.164500 | 1.0 | 0.604573 | 0.764868 | -4.664067 | 0.303660 | 2.668479 | 0.274397 |
| max | 223.361000 | 588.518000 | 199.020000 | 0.033160 | 0.000260 | 0.021440 | 0.019580 | 0.064330 | 0.119080 | 1.302000 | ... | 0.169420 | 0.314820 | 29.928000 | 1.0 | 0.685151 | 0.825288 | -2.434031 | 0.450493 | 3.671155 | 0.527367 |
8 rows × 23 columns
dataset2[dataset2['status']==0].describe() #Study for the people without of Parkinsons disease
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | Shimmer:DDA | NHR | HNR | status | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | ... | 48.000000 | 48.000000 | 48.00000 | 48.0 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 | 48.000000 |
| mean | 181.937771 | 223.636750 | 145.207292 | 0.003866 | 0.000023 | 0.001925 | 0.002056 | 0.005776 | 0.017615 | 0.162958 | ... | 0.028511 | 0.011483 | 24.67875 | 0.0 | 0.442552 | 0.695716 | -6.759264 | 0.160292 | 2.154491 | 0.123017 |
| std | 52.731067 | 96.727067 | 58.757070 | 0.002055 | 0.000015 | 0.001066 | 0.000943 | 0.003199 | 0.005544 | 0.057822 | ... | 0.010368 | 0.019088 | 3.43454 | 0.0 | 0.092199 | 0.051346 | 0.642782 | 0.062982 | 0.310269 | 0.044820 |
| min | 110.739000 | 113.597000 | 74.287000 | 0.001780 | 0.000007 | 0.000920 | 0.001060 | 0.002760 | 0.009540 | 0.085000 | ... | 0.014030 | 0.000650 | 17.88300 | 0.0 | 0.256570 | 0.626710 | -7.964984 | 0.006274 | 1.423287 | 0.044539 |
| 25% | 120.947500 | 139.413250 | 98.243750 | 0.002655 | 0.000010 | 0.001332 | 0.001480 | 0.003998 | 0.014475 | 0.129000 | ... | 0.022060 | 0.004188 | 22.99325 | 0.0 | 0.372126 | 0.654291 | -7.257665 | 0.120623 | 1.974217 | 0.094658 |
| 50% | 198.996000 | 231.161500 | 113.938500 | 0.003355 | 0.000025 | 0.001625 | 0.001775 | 0.004875 | 0.016705 | 0.154000 | ... | 0.026330 | 0.004825 | 24.99700 | 0.0 | 0.435368 | 0.682527 | -6.826448 | 0.167356 | 2.129510 | 0.115119 |
| 75% | 229.077000 | 251.239250 | 199.183000 | 0.004530 | 0.000030 | 0.001907 | 0.002228 | 0.005725 | 0.020210 | 0.189250 | ... | 0.034540 | 0.009213 | 26.13925 | 0.0 | 0.507748 | 0.742284 | -6.350146 | 0.193766 | 2.339487 | 0.147761 |
| max | 260.105000 | 592.030000 | 239.170000 | 0.013600 | 0.000080 | 0.006240 | 0.005640 | 0.018730 | 0.040870 | 0.405000 | ... | 0.070080 | 0.107150 | 33.04700 | 0.0 | 0.663842 | 0.785714 | -5.198864 | 0.291954 | 2.882450 | 0.252404 |
8 rows × 23 columns
dataset2.groupby(by=['status']).count() # Another way of finding the unique values on the python
print(dataset2.groupby(by=['status']).mean())
# Groupby() is the concept used,when dealing with large types of the values (binding the rows into groups depending in the features on the dataset)
MDVP:Fo(Hz) MDVP:Fhi(Hz) MDVP:Flo(Hz) MDVP:Jitter(%) \
status
0 181.937771 223.636750 145.207292 0.003866
1 145.180762 188.441463 106.893558 0.006989
MDVP:Jitter(Abs) MDVP:RAP MDVP:PPQ Jitter:DDP MDVP:Shimmer \
status
0 0.000023 0.001925 0.002056 0.005776 0.017615
1 0.000051 0.003757 0.003900 0.011273 0.033658
MDVP:Shimmer(dB) ... MDVP:APQ Shimmer:DDA NHR HNR \
status ...
0 0.162958 ... 0.013305 0.028511 0.011483 24.678750
1 0.321204 ... 0.027600 0.053027 0.029211 20.974048
RPDE DFA spread1 spread2 D2 PPE
status
0 0.442552 0.695716 -6.759264 0.160292 2.154491 0.123017
1 0.516816 0.725408 -5.333420 0.248133 2.456058 0.233828
[2 rows x 22 columns]
dataset1.groupby(by=['status']).mean() # Mean study based on the status
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | MDVP:APQ | Shimmer:DDA | NHR | HNR | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| status | |||||||||||||||||||||
| 0 | 181.937771 | 223.636750 | 145.207292 | 0.003866 | 0.000023 | 0.001925 | 0.002056 | 0.005776 | 0.017615 | 0.162958 | ... | 0.013305 | 0.028511 | 0.011483 | 24.678750 | 0.442552 | 0.695716 | -6.759264 | 0.160292 | 2.154491 | 0.123017 |
| 1 | 145.180762 | 188.441463 | 106.893558 | 0.006989 | 0.000051 | 0.003757 | 0.003900 | 0.011273 | 0.033658 | 0.321204 | ... | 0.027600 | 0.053027 | 0.029211 | 20.974048 | 0.516816 | 0.725408 | -5.333420 | 0.248133 | 2.456058 | 0.233828 |
2 rows × 22 columns
dataset2.groupby(by=['status']).median() # Median study based on the status
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | MDVP:APQ | Shimmer:DDA | NHR | HNR | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| status | |||||||||||||||||||||
| 0 | 198.996 | 231.1615 | 113.9385 | 0.003355 | 0.000025 | 0.001625 | 0.001775 | 0.004875 | 0.016705 | 0.154 | ... | 0.013015 | 0.02633 | 0.004825 | 24.997 | 0.435368 | 0.682527 | -6.826448 | 0.167356 | 2.129510 | 0.115119 |
| 1 | 145.174 | 163.3350 | 99.7700 | 0.005440 | 0.000040 | 0.002840 | 0.003140 | 0.008530 | 0.028380 | 0.263 | ... | 0.021570 | 0.04451 | 0.016580 | 21.414 | 0.530529 | 0.726652 | -5.440040 | 0.240875 | 2.439597 | 0.222716 |
2 rows × 22 columns
#Study of the dataset through the graph
sns.pairplot(data= dataset2)
<seaborn.axisgrid.PairGrid at 0xd9b2828>
sns.pairplot(data= dataset2,hue = "status")
C:\Users\HP\Anaconda3\lib\site-packages\statsmodels\nonparametric\kde.py:488: RuntimeWarning: invalid value encountered in true_divide binned = fast_linbin(X, a, b, gridsize) / (delta * nobs) C:\Users\HP\Anaconda3\lib\site-packages\statsmodels\nonparametric\kdetools.py:34: RuntimeWarning: invalid value encountered in double_scalars FAC1 = 2*(np.pi*bw/RANGE)**2
<seaborn.axisgrid.PairGrid at 0x211900f0>
from sklearn.model_selection import train_test_split
X = dataset2.drop(['status'], axis =1)
#X
#print(X.shape)
y= dataset2['status']
#print(y.shape) #(195,)
#y = dataset1[['status','HNR']]
#print(y.shape) # (195, 2)
X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.30)
X_train
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | MDVP:APQ | Shimmer:DDA | NHR | HNR | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 51 | 126.344 | 134.231 | 112.773 | 0.00448 | 0.000040 | 0.00131 | 0.00169 | 0.00393 | 0.02033 | 0.185 | ... | 0.01614 | 0.03429 | 0.00474 | 25.030 | 0.507504 | 0.760361 | -6.689151 | 0.291954 | 2.431854 | 0.105993 |
| 158 | 126.144 | 154.284 | 97.543 | 0.00975 | 0.000080 | 0.00593 | 0.00454 | 0.01778 | 0.02852 | 0.266 | ... | 0.02157 | 0.04499 | 0.03828 | 21.534 | 0.635015 | 0.627337 | -5.070096 | 0.280091 | 2.892300 | 0.249703 |
| 139 | 116.150 | 131.731 | 109.815 | 0.00381 | 0.000030 | 0.00181 | 0.00232 | 0.00542 | 0.03026 | 0.267 | ... | 0.02770 | 0.04543 | 0.01827 | 18.801 | 0.624811 | 0.696049 | -5.866357 | 0.233070 | 2.445646 | 0.184985 |
| 145 | 223.361 | 263.872 | 87.638 | 0.00352 | 0.000020 | 0.00169 | 0.00188 | 0.00506 | 0.02536 | 0.225 | ... | 0.01909 | 0.04137 | 0.01493 | 20.366 | 0.566849 | 0.574282 | -5.456811 | 0.345238 | 2.840556 | 0.232861 |
| 118 | 178.285 | 442.824 | 82.063 | 0.00462 | 0.000030 | 0.00157 | 0.00194 | 0.00472 | 0.01279 | 0.129 | ... | 0.01151 | 0.01851 | 0.00856 | 25.020 | 0.470422 | 0.655239 | -4.913137 | 0.393056 | 2.816781 | 0.251972 |
| 191 | 209.516 | 253.017 | 89.488 | 0.00564 | 0.000030 | 0.00331 | 0.00292 | 0.00994 | 0.02751 | 0.263 | ... | 0.01879 | 0.04812 | 0.01810 | 19.147 | 0.431674 | 0.683244 | -6.195325 | 0.129303 | 2.784312 | 0.168895 |
| 113 | 210.141 | 232.706 | 185.258 | 0.00534 | 0.000030 | 0.00321 | 0.00280 | 0.00964 | 0.01680 | 0.149 | ... | 0.01301 | 0.02583 | 0.00620 | 23.671 | 0.441097 | 0.722254 | -5.963040 | 0.250283 | 2.489191 | 0.177807 |
| 73 | 112.014 | 588.518 | 107.024 | 0.00533 | 0.000050 | 0.00268 | 0.00329 | 0.00805 | 0.02448 | 0.226 | ... | 0.01956 | 0.04120 | 0.00623 | 24.178 | 0.509127 | 0.789532 | -5.389129 | 0.306636 | 1.928708 | 0.225461 |
| 168 | 197.569 | 217.627 | 90.794 | 0.00803 | 0.000040 | 0.00490 | 0.00448 | 0.01470 | 0.02177 | 0.189 | ... | 0.01439 | 0.03836 | 0.01337 | 19.269 | 0.372222 | 0.725216 | -5.736781 | 0.164529 | 2.882450 | 0.202879 |
| 0 | 119.992 | 157.302 | 74.997 | 0.00784 | 0.000070 | 0.00370 | 0.00554 | 0.01109 | 0.04374 | 0.426 | ... | 0.02971 | 0.06545 | 0.02211 | 21.033 | 0.414783 | 0.815285 | -4.813031 | 0.266482 | 2.301442 | 0.284654 |
| 178 | 148.790 | 158.359 | 138.990 | 0.00309 | 0.000020 | 0.00152 | 0.00186 | 0.00456 | 0.01574 | 0.142 | ... | 0.01309 | 0.02518 | 0.00488 | 24.412 | 0.402591 | 0.762508 | -6.311987 | 0.182459 | 2.251553 | 0.160306 |
| 123 | 182.018 | 197.173 | 79.187 | 0.00842 | 0.000050 | 0.00506 | 0.00449 | 0.01517 | 0.02503 | 0.231 | ... | 0.01931 | 0.04115 | 0.01813 | 18.784 | 0.589956 | 0.732903 | -5.445140 | 0.142466 | 2.174306 | 0.215558 |
| 42 | 237.226 | 247.326 | 225.227 | 0.00298 | 0.000010 | 0.00169 | 0.00182 | 0.00507 | 0.01752 | 0.164 | ... | 0.01133 | 0.03104 | 0.00740 | 22.736 | 0.305062 | 0.654172 | -7.310550 | 0.098648 | 2.416838 | 0.095032 |
| 57 | 117.274 | 129.916 | 110.402 | 0.00752 | 0.000060 | 0.00299 | 0.00469 | 0.00898 | 0.02293 | 0.221 | ... | 0.01948 | 0.03568 | 0.00681 | 22.817 | 0.530529 | 0.817756 | -4.608260 | 0.290024 | 2.021591 | 0.314464 |
| 44 | 243.439 | 250.912 | 232.435 | 0.00210 | 0.000009 | 0.00109 | 0.00137 | 0.00327 | 0.01419 | 0.126 | ... | 0.01033 | 0.02330 | 0.00454 | 25.368 | 0.438296 | 0.635285 | -7.057869 | 0.091608 | 2.330716 | 0.091470 |
| 93 | 152.125 | 161.469 | 76.596 | 0.00382 | 0.000030 | 0.00191 | 0.00226 | 0.00574 | 0.05925 | 0.637 | ... | 0.04398 | 0.10024 | 0.01211 | 20.969 | 0.447456 | 0.697790 | -6.152551 | 0.173520 | 2.080121 | 0.160809 |
| 45 | 242.852 | 255.034 | 227.911 | 0.00225 | 0.000009 | 0.00117 | 0.00139 | 0.00350 | 0.01494 | 0.134 | ... | 0.01014 | 0.02542 | 0.00476 | 25.032 | 0.431285 | 0.638928 | -6.995820 | 0.102083 | 2.365800 | 0.102706 |
| 34 | 203.184 | 211.526 | 196.160 | 0.00178 | 0.000009 | 0.00094 | 0.00106 | 0.00283 | 0.00958 | 0.085 | ... | 0.00726 | 0.01403 | 0.00065 | 33.047 | 0.340068 | 0.741899 | -7.964984 | 0.163519 | 1.423287 | 0.044539 |
| 108 | 151.989 | 157.339 | 132.857 | 0.00174 | 0.000010 | 0.00075 | 0.00096 | 0.00225 | 0.01024 | 0.093 | ... | 0.00993 | 0.01364 | 0.00238 | 29.928 | 0.311369 | 0.676066 | -6.739151 | 0.160686 | 2.296873 | 0.115130 |
| 97 | 125.036 | 143.946 | 116.187 | 0.01280 | 0.000100 | 0.00743 | 0.00623 | 0.02228 | 0.03886 | 0.342 | ... | 0.03088 | 0.06406 | 0.08151 | 15.338 | 0.629574 | 0.714485 | -4.020042 | 0.265315 | 2.671825 | 0.340623 |
| 172 | 110.739 | 113.597 | 100.139 | 0.00356 | 0.000030 | 0.00170 | 0.00200 | 0.00510 | 0.01484 | 0.133 | ... | 0.01285 | 0.02261 | 0.00430 | 26.550 | 0.369090 | 0.776158 | -6.085567 | 0.192375 | 1.889002 | 0.174152 |
| 82 | 100.960 | 110.019 | 95.628 | 0.00606 | 0.000060 | 0.00351 | 0.00348 | 0.01053 | 0.02427 | 0.216 | ... | 0.01751 | 0.04114 | 0.01237 | 20.536 | 0.554610 | 0.787896 | -5.022288 | 0.146948 | 2.428306 | 0.264666 |
| 11 | 91.904 | 115.871 | 86.292 | 0.00540 | 0.000060 | 0.00281 | 0.00336 | 0.00844 | 0.02752 | 0.249 | ... | 0.02214 | 0.04272 | 0.01141 | 21.414 | 0.583390 | 0.792520 | -4.960234 | 0.363566 | 2.642476 | 0.275931 |
| 134 | 106.516 | 112.777 | 93.105 | 0.00589 | 0.000060 | 0.00291 | 0.00319 | 0.00873 | 0.04932 | 0.441 | ... | 0.03651 | 0.08050 | 0.03031 | 17.060 | 0.637814 | 0.744064 | -5.301321 | 0.320385 | 2.375138 | 0.243080 |
| 189 | 201.774 | 262.707 | 78.228 | 0.00694 | 0.000030 | 0.00412 | 0.00396 | 0.01235 | 0.02574 | 0.255 | ... | 0.01758 | 0.04363 | 0.04441 | 19.368 | 0.508479 | 0.683761 | -6.934474 | 0.159890 | 2.316346 | 0.112838 |
| 127 | 166.888 | 198.966 | 79.512 | 0.00638 | 0.000040 | 0.00368 | 0.00351 | 0.01104 | 0.02857 | 0.257 | ... | 0.02301 | 0.04641 | 0.01796 | 18.330 | 0.585169 | 0.736964 | -5.825257 | 0.115697 | 1.996146 | 0.196535 |
| 78 | 95.385 | 102.145 | 90.264 | 0.00608 | 0.000060 | 0.00331 | 0.00332 | 0.00994 | 0.03202 | 0.263 | ... | 0.02455 | 0.05408 | 0.01062 | 21.875 | 0.644954 | 0.779612 | -5.115212 | 0.249494 | 2.017753 | 0.260015 |
| 129 | 120.078 | 126.632 | 105.667 | 0.00270 | 0.000020 | 0.00116 | 0.00135 | 0.00349 | 0.01022 | 0.090 | ... | 0.00903 | 0.01428 | 0.00487 | 26.369 | 0.491345 | 0.718839 | -5.892061 | 0.195976 | 2.108873 | 0.183572 |
| 185 | 116.286 | 177.291 | 96.983 | 0.00314 | 0.000030 | 0.00134 | 0.00192 | 0.00403 | 0.01564 | 0.136 | ... | 0.01691 | 0.02001 | 0.00737 | 24.199 | 0.598515 | 0.654331 | -5.592584 | 0.133917 | 2.058658 | 0.214346 |
| 146 | 169.774 | 191.759 | 151.451 | 0.01568 | 0.000090 | 0.00863 | 0.00946 | 0.02589 | 0.08143 | 0.821 | ... | 0.08808 | 0.11411 | 0.07530 | 12.359 | 0.561610 | 0.793509 | -3.297668 | 0.414758 | 3.413649 | 0.457533 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 64 | 229.401 | 252.221 | 221.156 | 0.00205 | 0.000009 | 0.00114 | 0.00113 | 0.00342 | 0.01457 | 0.129 | ... | 0.01016 | 0.02308 | 0.00300 | 26.415 | 0.276850 | 0.673636 | -7.496264 | 0.056844 | 2.003032 | 0.073581 |
| 102 | 139.224 | 586.567 | 66.157 | 0.03011 | 0.000220 | 0.01854 | 0.01628 | 0.05563 | 0.09419 | 0.930 | ... | 0.06023 | 0.16654 | 0.25930 | 10.489 | 0.596362 | 0.641418 | -3.269487 | 0.270641 | 2.690917 | 0.444774 |
| 91 | 151.955 | 163.335 | 147.226 | 0.00419 | 0.000030 | 0.00224 | 0.00227 | 0.00672 | 0.07959 | 0.772 | ... | 0.05690 | 0.13262 | 0.01658 | 19.664 | 0.501037 | 0.714360 | -6.411497 | 0.207156 | 2.344876 | 0.134120 |
| 61 | 223.365 | 238.987 | 98.664 | 0.00264 | 0.000010 | 0.00154 | 0.00151 | 0.00461 | 0.01906 | 0.165 | ... | 0.01340 | 0.03039 | 0.00301 | 26.138 | 0.447979 | 0.686264 | -7.293801 | 0.086372 | 2.321560 | 0.098555 |
| 14 | 152.845 | 163.305 | 75.836 | 0.00294 | 0.000020 | 0.00121 | 0.00149 | 0.00364 | 0.01828 | 0.158 | ... | 0.01246 | 0.03191 | 0.00609 | 24.922 | 0.474791 | 0.654027 | -6.105098 | 0.203653 | 2.125618 | 0.170100 |
| 95 | 157.447 | 163.267 | 149.605 | 0.00369 | 0.000020 | 0.00201 | 0.00197 | 0.00602 | 0.03272 | 0.283 | ... | 0.02571 | 0.05439 | 0.01018 | 21.693 | 0.447285 | 0.705658 | -6.247076 | 0.180528 | 2.344348 | 0.164916 |
| 67 | 136.969 | 166.607 | 66.004 | 0.00923 | 0.000070 | 0.00507 | 0.00463 | 0.01520 | 0.03111 | 0.308 | ... | 0.02603 | 0.04914 | 0.02659 | 19.979 | 0.498133 | 0.729067 | -5.324574 | 0.205660 | 2.291558 | 0.226247 |
| 157 | 117.963 | 134.209 | 100.757 | 0.01813 | 0.000150 | 0.01117 | 0.00718 | 0.03351 | 0.04912 | 0.438 | ... | 0.02916 | 0.07830 | 0.10748 | 19.075 | 0.630547 | 0.646786 | -3.444478 | 0.303214 | 2.964568 | 0.261305 |
| 111 | 208.519 | 220.315 | 199.020 | 0.00609 | 0.000030 | 0.00368 | 0.00339 | 0.01105 | 0.01761 | 0.155 | ... | 0.01307 | 0.02855 | 0.00830 | 22.407 | 0.338097 | 0.712466 | -6.471427 | 0.184378 | 2.502336 | 0.136390 |
| 138 | 112.239 | 126.609 | 104.095 | 0.00472 | 0.000040 | 0.00238 | 0.00290 | 0.00715 | 0.05643 | 0.517 | ... | 0.04451 | 0.09211 | 0.02629 | 17.366 | 0.640945 | 0.701404 | -5.634576 | 0.306014 | 2.419253 | 0.209191 |
| 33 | 202.266 | 211.604 | 197.079 | 0.00180 | 0.000009 | 0.00093 | 0.00107 | 0.00278 | 0.00954 | 0.085 | ... | 0.00719 | 0.01407 | 0.00072 | 32.684 | 0.368535 | 0.742133 | -7.695734 | 0.178540 | 1.544609 | 0.056141 |
| 52 | 128.001 | 138.052 | 122.080 | 0.00436 | 0.000030 | 0.00137 | 0.00166 | 0.00411 | 0.02297 | 0.210 | ... | 0.01677 | 0.03969 | 0.00481 | 24.692 | 0.459766 | 0.766204 | -7.072419 | 0.220434 | 1.972297 | 0.119308 |
| 75 | 110.707 | 122.611 | 105.007 | 0.00516 | 0.000050 | 0.00277 | 0.00289 | 0.00831 | 0.02215 | 0.206 | ... | 0.01715 | 0.03851 | 0.00472 | 25.197 | 0.463514 | 0.807217 | -5.477592 | 0.315074 | 1.862092 | 0.228624 |
| 101 | 128.451 | 150.449 | 75.632 | 0.01551 | 0.000120 | 0.00905 | 0.00909 | 0.02716 | 0.06170 | 0.584 | ... | 0.05174 | 0.09669 | 0.11843 | 15.060 | 0.639808 | 0.643327 | -4.202730 | 0.310163 | 2.638279 | 0.356881 |
| 122 | 138.190 | 203.522 | 83.340 | 0.00704 | 0.000050 | 0.00406 | 0.00398 | 0.01218 | 0.04479 | 0.441 | ... | 0.03220 | 0.07761 | 0.01968 | 18.305 | 0.538016 | 0.741480 | -5.418787 | 0.160267 | 2.090438 | 0.229892 |
| 144 | 202.544 | 241.350 | 164.168 | 0.00254 | 0.000010 | 0.00100 | 0.00133 | 0.00301 | 0.02662 | 0.228 | ... | 0.02006 | 0.04426 | 0.01049 | 20.680 | 0.497480 | 0.630409 | -6.132663 | 0.220617 | 2.576563 | 0.159777 |
| 46 | 245.510 | 262.090 | 231.848 | 0.00235 | 0.000010 | 0.00127 | 0.00148 | 0.00380 | 0.01608 | 0.141 | ... | 0.01149 | 0.02719 | 0.00476 | 24.602 | 0.467489 | 0.631653 | -7.156076 | 0.127642 | 2.392122 | 0.097336 |
| 71 | 136.358 | 176.595 | 65.750 | 0.00971 | 0.000070 | 0.00534 | 0.00478 | 0.01601 | 0.04978 | 0.483 | ... | 0.03736 | 0.08247 | 0.03361 | 18.570 | 0.543299 | 0.733232 | -5.207985 | 0.224852 | 2.642276 | 0.242981 |
| 124 | 156.239 | 195.107 | 79.820 | 0.00694 | 0.000040 | 0.00403 | 0.00395 | 0.01209 | 0.02343 | 0.224 | ... | 0.01720 | 0.03867 | 0.02020 | 19.196 | 0.618663 | 0.728421 | -5.944191 | 0.143359 | 1.929715 | 0.181988 |
| 16 | 144.188 | 349.259 | 82.764 | 0.00544 | 0.000040 | 0.00211 | 0.00292 | 0.00632 | 0.02047 | 0.192 | ... | 0.02074 | 0.02908 | 0.01859 | 22.333 | 0.567380 | 0.644692 | -5.440040 | 0.239764 | 2.264501 | 0.218164 |
| 77 | 110.568 | 125.394 | 106.821 | 0.00462 | 0.000040 | 0.00226 | 0.00280 | 0.00677 | 0.02199 | 0.197 | ... | 0.01636 | 0.03852 | 0.00420 | 25.820 | 0.429484 | 0.816340 | -5.391029 | 0.250572 | 1.777901 | 0.232744 |
| 3 | 116.676 | 137.871 | 111.366 | 0.00997 | 0.000090 | 0.00502 | 0.00698 | 0.01505 | 0.05492 | 0.517 | ... | 0.03772 | 0.08771 | 0.01353 | 20.644 | 0.434969 | 0.819235 | -4.117501 | 0.334147 | 2.405554 | 0.368975 |
| 86 | 178.222 | 202.450 | 141.047 | 0.00321 | 0.000020 | 0.00163 | 0.00194 | 0.00488 | 0.03759 | 0.327 | ... | 0.02784 | 0.06219 | 0.03151 | 15.924 | 0.598714 | 0.712199 | -6.366916 | 0.335753 | 2.654271 | 0.144614 |
| 19 | 156.405 | 189.398 | 142.822 | 0.00768 | 0.000050 | 0.00372 | 0.00399 | 0.01116 | 0.03995 | 0.348 | ... | 0.04310 | 0.05164 | 0.03365 | 17.153 | 0.649554 | 0.686080 | -4.554466 | 0.340176 | 2.856676 | 0.322111 |
| 68 | 143.533 | 162.215 | 65.809 | 0.01101 | 0.000080 | 0.00647 | 0.00467 | 0.01941 | 0.05384 | 0.478 | ... | 0.03392 | 0.09455 | 0.04882 | 20.338 | 0.513237 | 0.731444 | -5.869750 | 0.151814 | 2.118496 | 0.185580 |
| 54 | 108.807 | 134.656 | 102.874 | 0.00761 | 0.000070 | 0.00349 | 0.00486 | 0.01046 | 0.02719 | 0.255 | ... | 0.02067 | 0.04450 | 0.01036 | 21.028 | 0.536009 | 0.819032 | -4.649573 | 0.205558 | 1.986899 | 0.316700 |
| 130 | 120.289 | 128.143 | 100.209 | 0.00492 | 0.000040 | 0.00269 | 0.00238 | 0.00808 | 0.01412 | 0.125 | ... | 0.01194 | 0.02110 | 0.01610 | 23.949 | 0.467160 | 0.724045 | -6.135296 | 0.203630 | 2.539724 | 0.169923 |
| 104 | 154.003 | 160.267 | 128.621 | 0.00183 | 0.000010 | 0.00076 | 0.00100 | 0.00229 | 0.01030 | 0.094 | ... | 0.00871 | 0.01406 | 0.00243 | 28.409 | 0.263654 | 0.691483 | -7.111576 | 0.144780 | 2.065477 | 0.093193 |
| 187 | 116.342 | 581.289 | 94.246 | 0.00267 | 0.000020 | 0.00115 | 0.00148 | 0.00345 | 0.01300 | 0.117 | ... | 0.01144 | 0.01892 | 0.00680 | 25.023 | 0.528485 | 0.663884 | -6.359018 | 0.116636 | 2.152083 | 0.138868 |
| 22 | 167.930 | 193.221 | 79.068 | 0.00442 | 0.000030 | 0.00220 | 0.00247 | 0.00661 | 0.04351 | 0.377 | ... | 0.04246 | 0.06685 | 0.01280 | 22.468 | 0.619060 | 0.679834 | -4.330956 | 0.262384 | 2.916777 | 0.285695 |
136 rows × 22 columns
X_test
| MDVP:Fo(Hz) | MDVP:Fhi(Hz) | MDVP:Flo(Hz) | MDVP:Jitter(%) | MDVP:Jitter(Abs) | MDVP:RAP | MDVP:PPQ | Jitter:DDP | MDVP:Shimmer | MDVP:Shimmer(dB) | ... | MDVP:APQ | Shimmer:DDA | NHR | HNR | RPDE | DFA | spread1 | spread2 | D2 | PPE | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 131 | 120.256 | 125.306 | 104.773 | 0.00407 | 0.00003 | 0.00224 | 0.00205 | 0.00671 | 0.01516 | 0.138 | ... | 0.01310 | 0.02164 | 0.01015 | 26.017 | 0.468621 | 0.735136 | -6.112667 | 0.217013 | 2.527742 | 0.170633 |
| 60 | 209.144 | 237.494 | 109.379 | 0.00282 | 0.00001 | 0.00147 | 0.00152 | 0.00442 | 0.01861 | 0.170 | ... | 0.01382 | 0.02925 | 0.00871 | 25.554 | 0.341788 | 0.678874 | -7.040508 | 0.066994 | 2.460791 | 0.101516 |
| 53 | 129.336 | 139.867 | 118.604 | 0.00490 | 0.00004 | 0.00165 | 0.00183 | 0.00495 | 0.02498 | 0.228 | ... | 0.01947 | 0.04188 | 0.00484 | 25.429 | 0.420383 | 0.785714 | -6.836811 | 0.269866 | 2.223719 | 0.147491 |
| 136 | 113.400 | 133.344 | 107.816 | 0.00451 | 0.00004 | 0.00219 | 0.00283 | 0.00658 | 0.04879 | 0.431 | ... | 0.04370 | 0.07154 | 0.02278 | 19.013 | 0.647900 | 0.708144 | -4.378916 | 0.300067 | 2.445502 | 0.259451 |
| 181 | 148.462 | 161.078 | 141.998 | 0.00397 | 0.00003 | 0.00202 | 0.00235 | 0.00605 | 0.01831 | 0.163 | ... | 0.01559 | 0.02849 | 0.00639 | 22.866 | 0.408598 | 0.768845 | -5.704053 | 0.216204 | 2.679185 | 0.197710 |
| 94 | 157.821 | 172.975 | 68.401 | 0.00358 | 0.00002 | 0.00196 | 0.00196 | 0.00587 | 0.03716 | 0.307 | ... | 0.02764 | 0.06185 | 0.00850 | 22.219 | 0.502380 | 0.712170 | -6.251425 | 0.188056 | 2.143851 | 0.160812 |
| 135 | 110.453 | 127.611 | 105.554 | 0.00494 | 0.00004 | 0.00244 | 0.00315 | 0.00731 | 0.04128 | 0.379 | ... | 0.03316 | 0.06688 | 0.02529 | 17.707 | 0.653427 | 0.706687 | -5.333619 | 0.322044 | 2.631793 | 0.228319 |
| 192 | 174.688 | 240.005 | 74.287 | 0.01360 | 0.00008 | 0.00624 | 0.00564 | 0.01873 | 0.02308 | 0.256 | ... | 0.01667 | 0.03804 | 0.10715 | 17.883 | 0.407567 | 0.655683 | -6.787197 | 0.158453 | 2.679772 | 0.131728 |
| 43 | 241.404 | 248.834 | 232.483 | 0.00281 | 0.00001 | 0.00157 | 0.00173 | 0.00470 | 0.01760 | 0.154 | ... | 0.01251 | 0.03017 | 0.00675 | 23.145 | 0.457702 | 0.634267 | -6.793547 | 0.158266 | 2.256699 | 0.117399 |
| 48 | 122.188 | 128.611 | 115.765 | 0.00524 | 0.00004 | 0.00169 | 0.00203 | 0.00507 | 0.01613 | 0.143 | ... | 0.01433 | 0.02566 | 0.00839 | 23.162 | 0.579597 | 0.733659 | -6.439398 | 0.266392 | 2.079922 | 0.133867 |
| 27 | 146.845 | 208.701 | 81.737 | 0.00496 | 0.00003 | 0.00250 | 0.00275 | 0.00749 | 0.01919 | 0.198 | ... | 0.01826 | 0.02650 | 0.01328 | 25.119 | 0.358773 | 0.726652 | -6.271690 | 0.196102 | 2.314209 | 0.162999 |
| 100 | 125.641 | 141.068 | 116.346 | 0.03316 | 0.00026 | 0.02144 | 0.01522 | 0.06433 | 0.09178 | 0.891 | ... | 0.06196 | 0.16074 | 0.31482 | 8.867 | 0.671299 | 0.656846 | -3.700544 | 0.260481 | 2.991063 | 0.370961 |
| 17 | 168.778 | 232.181 | 75.603 | 0.00718 | 0.00004 | 0.00284 | 0.00387 | 0.00853 | 0.03327 | 0.348 | ... | 0.03430 | 0.04322 | 0.02919 | 20.376 | 0.631099 | 0.605417 | -2.931070 | 0.434326 | 3.007463 | 0.430788 |
| 143 | 202.805 | 231.508 | 86.232 | 0.00370 | 0.00002 | 0.00189 | 0.00211 | 0.00568 | 0.01997 | 0.180 | ... | 0.01506 | 0.03350 | 0.02010 | 18.687 | 0.536102 | 0.632631 | -5.898673 | 0.213353 | 2.470746 | 0.189032 |
| 28 | 155.358 | 227.383 | 80.055 | 0.00310 | 0.00002 | 0.00159 | 0.00176 | 0.00476 | 0.01718 | 0.161 | ... | 0.01661 | 0.02307 | 0.00677 | 25.970 | 0.470478 | 0.676258 | -7.120925 | 0.279789 | 2.241742 | 0.108514 |
| 20 | 153.848 | 165.738 | 65.782 | 0.00840 | 0.00005 | 0.00428 | 0.00450 | 0.01285 | 0.03810 | 0.328 | ... | 0.04055 | 0.05000 | 0.03871 | 17.536 | 0.660125 | 0.704087 | -4.095442 | 0.262564 | 2.739710 | 0.365391 |
| 85 | 180.978 | 200.125 | 155.495 | 0.00406 | 0.00002 | 0.00220 | 0.00244 | 0.00659 | 0.03852 | 0.331 | ... | 0.02877 | 0.06321 | 0.02782 | 16.176 | 0.583574 | 0.727747 | -5.657899 | 0.315903 | 3.098256 | 0.200423 |
| 163 | 112.150 | 131.669 | 97.527 | 0.00519 | 0.00005 | 0.00291 | 0.00284 | 0.00873 | 0.01756 | 0.155 | ... | 0.01363 | 0.02902 | 0.01435 | 21.219 | 0.557045 | 0.673086 | -5.617124 | 0.184896 | 1.871871 | 0.212386 |
| 159 | 127.930 | 138.752 | 112.173 | 0.00605 | 0.00005 | 0.00321 | 0.00318 | 0.00962 | 0.03235 | 0.339 | ... | 0.03105 | 0.04079 | 0.02663 | 19.651 | 0.654945 | 0.675865 | -5.498456 | 0.234196 | 2.103014 | 0.216638 |
| 18 | 153.046 | 175.829 | 68.623 | 0.00742 | 0.00005 | 0.00364 | 0.00432 | 0.01092 | 0.05517 | 0.542 | ... | 0.05767 | 0.07413 | 0.03160 | 17.280 | 0.665318 | 0.719467 | -3.949079 | 0.357870 | 3.109010 | 0.377429 |
| 80 | 96.106 | 108.664 | 84.510 | 0.00694 | 0.00007 | 0.00389 | 0.00415 | 0.01168 | 0.04024 | 0.364 | ... | 0.02876 | 0.06799 | 0.01823 | 19.055 | 0.544805 | 0.770466 | -4.441519 | 0.155097 | 2.645959 | 0.327978 |
| 58 | 116.879 | 131.897 | 108.153 | 0.00788 | 0.00007 | 0.00334 | 0.00493 | 0.01003 | 0.02645 | 0.265 | ... | 0.02137 | 0.04183 | 0.00786 | 22.603 | 0.540049 | 0.813432 | -4.476755 | 0.262633 | 1.827012 | 0.326197 |
| 182 | 149.818 | 163.417 | 144.786 | 0.00336 | 0.00002 | 0.00174 | 0.00198 | 0.00521 | 0.02145 | 0.198 | ... | 0.01666 | 0.03464 | 0.00595 | 23.008 | 0.329577 | 0.757180 | -6.277170 | 0.109397 | 2.209021 | 0.156368 |
| 23 | 173.917 | 192.735 | 86.180 | 0.00476 | 0.00003 | 0.00221 | 0.00258 | 0.00663 | 0.04192 | 0.364 | ... | 0.03772 | 0.06562 | 0.01840 | 20.422 | 0.537264 | 0.686894 | -5.248776 | 0.210279 | 2.547508 | 0.253556 |
| 56 | 110.417 | 131.067 | 103.370 | 0.00784 | 0.00007 | 0.00352 | 0.00514 | 0.01056 | 0.03715 | 0.334 | ... | 0.02802 | 0.06097 | 0.00969 | 21.422 | 0.541781 | 0.821364 | -4.438453 | 0.238298 | 1.922940 | 0.335041 |
| 175 | 115.380 | 123.109 | 108.634 | 0.00332 | 0.00003 | 0.00160 | 0.00199 | 0.00480 | 0.01503 | 0.137 | ... | 0.01133 | 0.02436 | 0.00401 | 26.005 | 0.405991 | 0.761255 | -5.966779 | 0.197938 | 1.974857 | 0.184067 |
| 30 | 197.076 | 206.896 | 192.055 | 0.00289 | 0.00001 | 0.00166 | 0.00168 | 0.00498 | 0.01098 | 0.097 | ... | 0.00802 | 0.01689 | 0.00339 | 26.775 | 0.422229 | 0.741367 | -7.348300 | 0.177551 | 1.743867 | 0.085569 |
| 193 | 198.764 | 396.961 | 74.904 | 0.00740 | 0.00004 | 0.00370 | 0.00390 | 0.01109 | 0.02296 | 0.241 | ... | 0.01588 | 0.03794 | 0.07223 | 19.020 | 0.451221 | 0.643956 | -6.744577 | 0.207454 | 2.138608 | 0.123306 |
| 36 | 177.876 | 192.921 | 168.013 | 0.00411 | 0.00002 | 0.00233 | 0.00241 | 0.00700 | 0.02126 | 0.189 | ... | 0.01612 | 0.03463 | 0.00586 | 23.216 | 0.360148 | 0.778834 | -6.149653 | 0.218037 | 2.477082 | 0.165827 |
| 167 | 260.105 | 264.919 | 237.303 | 0.00339 | 0.00001 | 0.00205 | 0.00186 | 0.00616 | 0.02030 | 0.197 | ... | 0.01367 | 0.03557 | 0.00910 | 21.083 | 0.440988 | 0.628058 | -7.517934 | 0.160414 | 1.881767 | 0.075587 |
| 105 | 149.689 | 160.368 | 133.608 | 0.00257 | 0.00002 | 0.00116 | 0.00134 | 0.00349 | 0.01346 | 0.126 | ... | 0.01059 | 0.01979 | 0.00578 | 27.421 | 0.365488 | 0.719974 | -6.997403 | 0.210279 | 1.994387 | 0.112878 |
| 24 | 163.656 | 200.841 | 76.779 | 0.00742 | 0.00005 | 0.00380 | 0.00390 | 0.01140 | 0.01659 | 0.164 | ... | 0.01497 | 0.02214 | 0.01778 | 23.831 | 0.397937 | 0.732479 | -5.557447 | 0.220890 | 2.692176 | 0.215961 |
| 179 | 148.143 | 155.982 | 135.041 | 0.00392 | 0.00003 | 0.00204 | 0.00231 | 0.00612 | 0.01450 | 0.131 | ... | 0.01263 | 0.02175 | 0.00540 | 23.683 | 0.398499 | 0.778349 | -5.711205 | 0.240875 | 2.845109 | 0.192730 |
| 1 | 122.400 | 148.650 | 113.819 | 0.00968 | 0.00008 | 0.00465 | 0.00696 | 0.01394 | 0.06134 | 0.626 | ... | 0.04368 | 0.09403 | 0.01929 | 19.085 | 0.458359 | 0.819521 | -4.075192 | 0.335590 | 2.486855 | 0.368674 |
| 173 | 113.715 | 116.443 | 96.913 | 0.00349 | 0.00003 | 0.00171 | 0.00203 | 0.00514 | 0.01472 | 0.133 | ... | 0.01148 | 0.02245 | 0.00478 | 26.547 | 0.380253 | 0.766700 | -5.943501 | 0.192150 | 1.852542 | 0.179677 |
| 133 | 118.747 | 123.723 | 109.836 | 0.00331 | 0.00003 | 0.00168 | 0.00171 | 0.00504 | 0.01043 | 0.099 | ... | 0.00903 | 0.01471 | 0.00504 | 25.619 | 0.482296 | 0.723096 | -6.448134 | 0.178713 | 2.034827 | 0.141422 |
| 72 | 120.080 | 139.710 | 111.208 | 0.00405 | 0.00003 | 0.00180 | 0.00220 | 0.00540 | 0.01706 | 0.152 | ... | 0.01345 | 0.02921 | 0.00442 | 25.742 | 0.495954 | 0.762959 | -5.791820 | 0.329066 | 2.205024 | 0.188180 |
| 160 | 114.238 | 124.393 | 77.022 | 0.00581 | 0.00005 | 0.00299 | 0.00316 | 0.00896 | 0.04009 | 0.406 | ... | 0.04114 | 0.04736 | 0.02073 | 20.437 | 0.653139 | 0.694571 | -5.185987 | 0.259229 | 2.151121 | 0.244948 |
| 92 | 148.272 | 164.989 | 142.299 | 0.00459 | 0.00003 | 0.00250 | 0.00256 | 0.00750 | 0.04190 | 0.383 | ... | 0.03051 | 0.07150 | 0.01914 | 18.780 | 0.454444 | 0.734504 | -5.952058 | 0.087840 | 2.344336 | 0.186489 |
| 161 | 115.322 | 135.738 | 107.802 | 0.00619 | 0.00005 | 0.00352 | 0.00329 | 0.01057 | 0.03273 | 0.325 | ... | 0.02931 | 0.04933 | 0.02810 | 19.388 | 0.577802 | 0.684373 | -5.283009 | 0.226528 | 2.442906 | 0.238281 |
| 170 | 244.990 | 272.210 | 239.170 | 0.00451 | 0.00002 | 0.00279 | 0.00237 | 0.00837 | 0.01897 | 0.181 | ... | 0.01255 | 0.03253 | 0.01049 | 21.528 | 0.522812 | 0.646818 | -7.304500 | 0.171088 | 2.095237 | 0.096220 |
| 140 | 170.368 | 268.796 | 79.543 | 0.00571 | 0.00003 | 0.00232 | 0.00269 | 0.00696 | 0.03273 | 0.281 | ... | 0.02824 | 0.05139 | 0.02485 | 18.540 | 0.677131 | 0.685057 | -4.796845 | 0.397749 | 2.963799 | 0.277227 |
| 116 | 158.219 | 442.557 | 71.948 | 0.00476 | 0.00003 | 0.00214 | 0.00207 | 0.00642 | 0.01458 | 0.148 | ... | 0.01312 | 0.01818 | 0.01554 | 26.356 | 0.450798 | 0.653823 | -6.051233 | 0.273280 | 2.640798 | 0.170106 |
| 96 | 159.116 | 168.913 | 144.811 | 0.00342 | 0.00002 | 0.00178 | 0.00184 | 0.00535 | 0.03381 | 0.307 | ... | 0.02809 | 0.05417 | 0.00852 | 22.663 | 0.366329 | 0.693429 | -6.417440 | 0.194627 | 2.473239 | 0.151709 |
| 142 | 198.458 | 219.290 | 148.691 | 0.00376 | 0.00002 | 0.00182 | 0.00215 | 0.00546 | 0.03527 | 0.297 | ... | 0.02530 | 0.06165 | 0.01728 | 18.702 | 0.606273 | 0.661735 | -5.585259 | 0.310746 | 2.465528 | 0.209863 |
| 162 | 114.554 | 126.778 | 91.121 | 0.00651 | 0.00006 | 0.00366 | 0.00340 | 0.01097 | 0.03658 | 0.369 | ... | 0.03091 | 0.05592 | 0.02707 | 18.954 | 0.685151 | 0.719576 | -5.529833 | 0.242750 | 2.408689 | 0.220520 |
| 153 | 121.345 | 139.644 | 98.250 | 0.00684 | 0.00006 | 0.00388 | 0.00332 | 0.01164 | 0.02534 | 0.241 | ... | 0.02056 | 0.04019 | 0.04179 | 21.520 | 0.566867 | 0.670475 | -4.865194 | 0.246404 | 2.013530 | 0.168581 |
| 119 | 217.116 | 233.481 | 93.978 | 0.00404 | 0.00002 | 0.00127 | 0.00128 | 0.00381 | 0.01299 | 0.124 | ... | 0.01075 | 0.02038 | 0.00681 | 24.581 | 0.462516 | 0.582710 | -5.517173 | 0.389295 | 2.925862 | 0.220657 |
| 32 | 198.383 | 215.203 | 193.104 | 0.00212 | 0.00001 | 0.00113 | 0.00135 | 0.00339 | 0.01263 | 0.111 | ... | 0.00951 | 0.01919 | 0.00119 | 30.775 | 0.465946 | 0.738703 | -7.067931 | 0.175181 | 1.512275 | 0.096320 |
| 184 | 116.848 | 217.552 | 99.503 | 0.00531 | 0.00005 | 0.00260 | 0.00346 | 0.00780 | 0.01795 | 0.163 | ... | 0.01756 | 0.02429 | 0.01179 | 22.085 | 0.663842 | 0.656516 | -5.198864 | 0.206768 | 2.120412 | 0.252404 |
| 59 | 114.847 | 271.314 | 104.680 | 0.00867 | 0.00008 | 0.00373 | 0.00520 | 0.01120 | 0.03225 | 0.350 | ... | 0.02519 | 0.05414 | 0.01143 | 21.660 | 0.547975 | 0.817396 | -4.609161 | 0.221711 | 1.831691 | 0.316395 |
| 112 | 204.664 | 221.300 | 189.621 | 0.00841 | 0.00004 | 0.00502 | 0.00485 | 0.01506 | 0.02378 | 0.210 | ... | 0.01767 | 0.03831 | 0.01316 | 21.305 | 0.498877 | 0.722085 | -4.876336 | 0.212054 | 2.376749 | 0.268144 |
| 126 | 138.145 | 197.238 | 81.114 | 0.00544 | 0.00004 | 0.00294 | 0.00327 | 0.00883 | 0.02791 | 0.246 | ... | 0.02259 | 0.04451 | 0.01794 | 18.178 | 0.623209 | 0.738245 | -5.540351 | 0.087165 | 1.821297 | 0.214075 |
| 188 | 114.563 | 119.167 | 86.647 | 0.00327 | 0.00003 | 0.00146 | 0.00184 | 0.00439 | 0.01185 | 0.106 | ... | 0.01095 | 0.01672 | 0.00703 | 24.775 | 0.555303 | 0.659132 | -6.710219 | 0.149694 | 1.913990 | 0.121777 |
| 117 | 170.756 | 450.247 | 79.032 | 0.00555 | 0.00003 | 0.00244 | 0.00261 | 0.00731 | 0.01725 | 0.175 | ... | 0.01652 | 0.02270 | 0.01802 | 25.690 | 0.486738 | 0.676023 | -4.597834 | 0.372114 | 2.975889 | 0.282780 |
| 25 | 104.400 | 206.002 | 77.968 | 0.00633 | 0.00006 | 0.00316 | 0.00375 | 0.00948 | 0.03767 | 0.381 | ... | 0.03780 | 0.05197 | 0.02887 | 22.066 | 0.522746 | 0.737948 | -5.571843 | 0.236853 | 2.846369 | 0.219514 |
| 90 | 166.605 | 206.008 | 78.032 | 0.00742 | 0.00004 | 0.00387 | 0.00453 | 0.01161 | 0.06640 | 0.634 | ... | 0.05114 | 0.10949 | 0.08725 | 11.744 | 0.653410 | 0.733165 | -4.508984 | 0.389232 | 3.317586 | 0.301952 |
| 141 | 208.083 | 253.792 | 91.802 | 0.00757 | 0.00004 | 0.00428 | 0.00428 | 0.01285 | 0.06725 | 0.571 | ... | 0.04464 | 0.12047 | 0.04238 | 15.648 | 0.606344 | 0.665945 | -5.410336 | 0.288917 | 2.665133 | 0.231723 |
| 137 | 113.166 | 130.270 | 100.673 | 0.00502 | 0.00004 | 0.00257 | 0.00312 | 0.00772 | 0.05279 | 0.476 | ... | 0.04134 | 0.08689 | 0.03690 | 16.747 | 0.625362 | 0.708617 | -4.654894 | 0.304107 | 2.672362 | 0.274387 |
59 rows × 22 columns
y_train
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..
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Name: status, Length: 136, dtype: int64
from sklearn.tree import DecisionTreeClassifier
dt_model1 = DecisionTreeClassifier(criterion = 'entropy',random_state =1)
# Model creation using the entropy method
dt_model1.fit(X_train,y_train)
# Fitting the train values inside the fit method
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=1,
splitter='best')
# calculating the accuracy value on the test and train datas
print(dt_model1.score(X_train,y_train)) # Using the train values
print(dt_model1.score(X_test,y_test)) # Using the test values
# 86% accuravy value is being achieved without applying any parameter conditions
1.0 0.8305084745762712
dt_model1 = DecisionTreeClassifier(criterion = 'entropy',max_depth = 12)
dt_model1.fit(X_train,y_train)
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=12,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
print(dt_model1.score(X_train,y_train)) # Using the train values
#Training error is zero in other words
print(dt_model1.score(X_test,y_test)) # Using the test values
# 86% of the test records are correctly classified
1.0 0.8305084745762712
# Regularizing part of the Decision Tree
for values in range(1,15):
dt_model1 = DecisionTreeClassifier(criterion = 'entropy',max_depth=values)
dt_model1.fit(X_train,y_train)
print("Training value accuracy_score for :",dt_model1.score(X_train,y_train),"Testing Value accuracy_value for :",dt_model1.score(X_test,y_test))
# For the max_depth of 4, the accuracy values are found to be good, avoiding the overfitting condition
Training value accuracy_score for : 0.8676470588235294 Testing Value accuracy_value for : 0.847457627118644 Training value accuracy_score for : 0.875 Testing Value accuracy_value for : 0.847457627118644 Training value accuracy_score for : 0.875 Testing Value accuracy_value for : 0.847457627118644 Training value accuracy_score for : 0.8897058823529411 Testing Value accuracy_value for : 0.7966101694915254 Training value accuracy_score for : 0.9411764705882353 Testing Value accuracy_value for : 0.8135593220338984 Training value accuracy_score for : 0.9779411764705882 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712 Training value accuracy_score for : 1.0 Testing Value accuracy_value for : 0.8305084745762712
from sklearn.feature_extraction.text import CountVectorizer
y_predict = dt_model1.predict(X_test)
dt_model1 = DecisionTreeClassifier(criterion = 'entropy',max_depth = 4)
dt_model1.fit(X_train,y_train)
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
print(dt_model1.score(X_train,y_train))
print(dt_model1.score(X_test,y_test))
0.8897058823529411 0.7966101694915254
from sklearn import metrics
print(metrics.confusion_matrix(y_test,y_predict))
# Dataframe creation for the heatmap
dataframe_for_heatmap = pd.DataFrame(metrics.confusion_matrix(y_test,y_predict),index=['Healthy','Parkinsons'],columns=['Healthy','Parkinsons'])
#print(type(dataframe_for_heatmap))
print(dataframe_for_heatmap)
[[ 9 5]
[ 5 40]]
Healthy Parkinsons
Healthy 9 5
Parkinsons 5 40
dataframe_for_heatmap
| Healthy | Parkinsons | |
|---|---|---|
| Healthy | 9 | 5 |
| Parkinsons | 5 | 40 |
# Heatmap design
sns.heatmap(data = dataframe_for_heatmap,annot = True)
plt.xlabel('Acutal')
plt.ylabel('Predicted')
plt.show()
print(pd.DataFrame(dt_model1.feature_importances_, columns=['Imp'],index =X_train.columns ))
Imp MDVP:Fo(Hz) 0.000000 MDVP:Fhi(Hz) 0.000000 MDVP:Flo(Hz) 0.251038 MDVP:Jitter(%) 0.000000 MDVP:Jitter(Abs) 0.000000 MDVP:RAP 0.071047 MDVP:PPQ 0.000000 Jitter:DDP 0.000000 MDVP:Shimmer 0.000000 MDVP:Shimmer(dB) 0.000000 Shimmer:APQ3 0.000000 Shimmer:APQ5 0.000000 MDVP:APQ 0.000000 Shimmer:DDA 0.000000 NHR 0.000000 HNR 0.000000 RPDE 0.000000 DFA 0.000000 spread1 0.237025 spread2 0.000000 D2 0.000000 PPE 0.440890
# Using min_sample_leaf as theonly parameter on the decision tree classifier
for values in range(1,15):
dt_model1 = DecisionTreeClassifier(criterion = 'entropy',min_samples_leaf=values)
dt_model1.fit(X_train,y_train)
print("Training value accuracy_score:",dt_model1.score(X_train,y_train),"Testing Value accuracy_value:",dt_model1.score(X_test,y_test))
# At the values of min_samples_leaf=6, the accuracy values for the training and test railings are found to be nearly matching and similar
Training value accuracy_score: 1.0 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9852941176470589 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9779411764705882 Testing Value accuracy_value: 0.8135593220338984 Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9705882352941176 Testing Value accuracy_value: 0.8305084745762712 Training value accuracy_score: 0.9558823529411765 Testing Value accuracy_value: 0.864406779661017 Training value accuracy_score: 0.9191176470588235 Testing Value accuracy_value: 0.8135593220338984 Training value accuracy_score: 0.9044117647058824 Testing Value accuracy_value: 0.847457627118644 Training value accuracy_score: 0.9044117647058824 Testing Value accuracy_value: 0.847457627118644 Training value accuracy_score: 0.9044117647058824 Testing Value accuracy_value: 0.847457627118644
dt_model1 = DecisionTreeClassifier(criterion = 'entropy',min_samples_leaf=6)
dt_model1.fit(X_train,y_train)
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=6, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=None,
splitter='best')
print(dt_model1.score(X_train,y_train))
print(dt_model1.score(X_test,y_test))
print(metrics.confusion_matrix(y_test,y_predict))
0.9705882352941176 0.8305084745762712 [[ 9 5] [ 5 40]]
from sklearn.ensemble import RandomForestClassifier
model2 = RandomForestClassifier(n_estimators=5)
model2.fit(X_train,y_train)
# 5 instances creation
RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
max_depth=None, max_features='auto', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=5, n_jobs=None,
oob_score=False, random_state=None, verbose=0,
warm_start=False)
model2.score(X_train,y_train)
0.9926470588235294
# For training set
test_predict = model2.predict(X_test)
model2.score(X_test,y_test)
# Everytime, when this technique is given run, they will not produce accurate results, they will keep on changing for every run
# Hence random_state seeding is required to produce the more accurate results
# For selecting that value, the below code follows
0.8983050847457628
from sklearn.ensemble import RandomForestClassifier
for x in range(1,14):
model2 = RandomForestClassifier(n_estimators=x,random_state=252)
model2.fit(X_train,y_train)
print("Training value accuracy_score :",model2.score(X_train,y_train))
test_predict = model2.predict(X_test)
print("Testing Value accuracy_value :",model2.score(X_test,y_test))
# 1) For the n_estimators for value = 5, we are getting the 'best accuracy score' when compared to other values, since
# on the syntax of the RandomForestClassifier(n_estimators =10), the general value is 10
Training value accuracy_score : 0.8897058823529411 Testing Value accuracy_value : 0.7966101694915254 Training value accuracy_score : 0.8970588235294118 Testing Value accuracy_value : 0.8305084745762712 Training value accuracy_score : 0.9558823529411765 Testing Value accuracy_value : 0.864406779661017 Training value accuracy_score : 0.9852941176470589 Testing Value accuracy_value : 0.9152542372881356 Training value accuracy_score : 0.9779411764705882 Testing Value accuracy_value : 0.9322033898305084 Training value accuracy_score : 0.9705882352941176 Testing Value accuracy_value : 0.864406779661017 Training value accuracy_score : 0.9926470588235294 Testing Value accuracy_value : 0.9152542372881356 Training value accuracy_score : 0.9779411764705882 Testing Value accuracy_value : 0.9322033898305084 Training value accuracy_score : 0.9852941176470589 Testing Value accuracy_value : 0.9152542372881356 Training value accuracy_score : 0.9852941176470589 Testing Value accuracy_value : 0.9322033898305084 Training value accuracy_score : 0.9926470588235294 Testing Value accuracy_value : 0.8983050847457628 Training value accuracy_score : 1.0 Testing Value accuracy_value : 0.9152542372881356 Training value accuracy_score : 1.0 Testing Value accuracy_value : 0.9152542372881356